A Survey on Ad Hoc Signal Processing: Applications, Challenges and State-of-the-Art Techniques

Pasha, Shahab; Ritz, Christian; Lundgren, Jan

doi:10.1109/isspit47144.2019.9001860

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…Ad hoc microphone arrays [1,3] consist of a set of recording devices (referred to as nodes [5]) randomly distributed in an acoustic environment to record an unknown acoustic scene with wide spatial coverage (Figure 1). The nodes can be identical [5] or different [6] in terms of their structure and number of elements [7,8] (Table 1).…”

Section: Distributed Signal Processingmentioning

confidence: 99%

“…With new portable devices, such as smartphones and tablets, conventional microphone arrays are no longer the main signal and speech acquisition platform; rather, distributed microphone arrays (also called ad hoc microphone arrays) formed by the joint analysis of randomly located independent recording devices such as laptops and cell phones are emerging recording platforms for various applications [1]. Conventional compact microphone arrays and recording devices are now processed within ad hoc arrays and are jointly analyzed with other such devices.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Pasha¹,

Lundgren²,

Ritz³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Given ubiquitous digital devices with recording capability, distributed microphone arrays are emerging recording tools for hands-free communications and spontaneous tele-conferencings. However, the analysis of signals recorded with diverse sampling rates, time delays, and qualities by distributed microphone arrays is not straightforward and entails important considerations. The crucial challenges include the unknown/changeable geometry of distributed arrays, asynchronous recording, sampling rate mismatch, and gain inconsistency. Researchers have recently proposed solutions to these problems for applications such as source localization and dereverberation, though there is less literature on real-time practical issues. This article reviews recent research on distributed signal processing techniques and applications. New applications benefitting from the wide coverage of distributed microphones are reviewed and their limitations are discussed. This survey does not cover partially or fully connected wireless acoustic sensor networks.

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Section: Distributed Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Pasha¹,

Lundgren²,

Ritz³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

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show abstract

“…Acoustic sensor networks (ASNs) are gaining increasing popularity thanks to their capacity to exploit information from various (wireless) and sometimes ad-hoc acoustic sensors. ASNs have become hosts to many applications [1], [2], e.g., source localization [3], event classification [4], speech enhancement [5], etc. These, in turn, can benefit from knowledge of source-dominated clusters formed by microphone-carrying nodes in the vicinity of distributed acoustic sources [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

Nelus

Glitza

Martin

2021

2021 29th European Signal Processing Conference (EUSIPCO)

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In this paper we introduce a realistic and challenging, multi-source and multi-room acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks. Our proposed clustering method is based on a single microphone per node and on unsupervised clustered federated learning which employs a light-weight autoencoder model. We present an improved clustering control strategy that takes into account the variability of the acoustic scene and allows the estimation of a dynamic range of clusters using reduced amounts of training data. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.

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Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments

Glitza,

Becker,

Nelus

et al. 2023

2023 31st European Signal Processing Conference (EUSIPCO)

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A Survey on Ad Hoc Signal Processing: Applications, Challenges and State-of-the-Art Techniques

Cited by 3 publications

References 38 publications

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments

Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments

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